code: def test_explain(
      mock_ale_explainer,
      features,
      input_dim,
      batch_size,
      custom_grid,
      num_grid_points,
  ):
      out_dim = mock_ale_explainer.predictor.out_dim
      X = np.random.rand(batch_size, input_dim)
  
      if features:
          n_features = len(features)
          if not custom_grid:
              grid_points = None
          else:
              grid_points = {
                  f: np.random.rand(num_grid_points) for f in features
              }
      else:
          n_features = input_dim
          if not custom_grid:
              grid_points = None
          else:
              grid_points = {
                  f: np.random.rand(num_grid_points)
                  for f in range(n_features)
              }
  
      exp = mock_ale_explainer.explain(
          X, features=features, grid_points=grid_points
      )
  
      # check that the length of all relevant attributes is the same as the number of features explained
      assert all(
          len(attr) == n_features
          for attr in (
              exp.ale_values,
              exp.feature_values,
              exp.feature_names,
              exp.feature_deciles,
              exp.ale0,
          )
      )
  
      assert len(exp.target_names) == out_dim
      for alev, featv in zip(exp.ale_values, exp.feature_values):
          assert alev.shape == (featv.shape[0], out_dim)
  
      if custom_grid:
          for i, f in enumerate(grid_points.keys()):
              # need to remove the first and last element just in case the feature values are extended
              # with the min & max feature value. Check if subset since some grid-points might have
              # been removed because of merging empty intervals.
              assert np.all(
                  np.isin(exp.feature_values[i][1:-1], grid_points[f])
              )
  
      assert isinstance(exp.constant_value, float)
  
      for a0 in exp.ale0:
          assert a0.shape == (out_dim,)
  
      assert exp.meta.keys() == DEFAULT_META_ALE.keys()
      assert exp.data.keys() == DEFAULT_DATA_ALE.keys()
  
imports: import re
  from copy import deepcopy
  import matplotlib.pyplot
  import numpy
  import pytest
  from numpy.testing import assert_allclose
  from pytest_lazyfixture import lazy_fixture
  from alibi.api.defaults import DEFAULT_DATA_ALE, DEFAULT_META_ALE
  from alibi.api.interfaces import Explanation
  from alibi.explainers.ale import _plot_one_ale_num, adaptive_grid, ale_num, get_quantiles, minimum_satisfied, plot_ale
label_desc: Use image node_id 6 for calling a global function with example usage: test_explain(mock_ale_explainer, features, input_dim, batch_size, custom_grid, num_grid_points) without return types